Author : Phillip Richard Palmer
Release : 2022
Genre : Bacteria
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Bacterial Persistence and Biofilms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bacterial Persistence and Biofilms write by Phillip Richard Palmer. This book was released on 2022. Bacterial Persistence and Biofilms available in PDF, EPUB and Kindle. Bacterial persistence is a transient state of dormancy exhibited by a small subset of cells that allow them to survive antibiotic treatment. This is a ubiquitous phenomenon exhibited by all bacteria to all known classes of antibiotics which can result in chronic and relapsing infections. Persister cells are bacteria that are metabolically tolerant, not genetically resistant to antibiotics. However, because they confer survival in the presence of antibiotics, they pose a risk of developing resistance. Antibiotic resistance is currently a world-wide problem as resistant infections are becoming more numerous and the discovery rate of novel antibiotics is decreasing. Biofilms contain increased levels of persisters due to the nutrient limiting conditions in the interior of the biofilm due to limiting diffusion rates through the dense extracellular polymeric matrix. The specific mechanisms that lead to persistence are redundant making them difficult to overcome pharmacologically. Stress responses are thought to push the cell towards a dormant state in response to either accidental damage from cellular processes, or to antibiotics. Toxin-antitoxin systems have been shown to increase levels of persister cells increasing the lag time after inoculation from overnight cultures. Persisters are difficult to study because they appear at such low frequencies in metabolically heterogenous populations. Experimenters are thus forced to choose between scale and resolution as single-cell experiments fail to observe sufficient numbers of cells. Many labs utilize methods of enriching for persisters, though the largest problem is that persisters cannot be identified except by surviving antibiotic treatments. This thesis discusses novel methods that can be used to study persistence and chronic bacterial infection. Recent advances in CRISPR biotechnology have created a large toolbox of molecular techniques. CRISPR-Cas9 is best known for creating gene knockouts with ease, but an enzymatically inactivated Cas9 has been developed and repurposed as a tool for gene knockdown. The gene target can be customized by modifying the CRISPR gRNA that is responsible for targeting the Cas9 to a gene by base-pair complementarity. This technology was validated in our lab by confirming knockdown of lacZ expression with blue-white selection. A detailed method for creating gRNA was reproduced from the original paper. Novel gRNAs were designed and cloned into the CRISPRi system to target the stringent response genes relA and spoT. These genes control the levels of the alarmone (p)ppGpp in the cell which when in abundance downregulates genes associated with growth and division and upregulates genes associated with survival. Targeting these genes with CRISPRi was explored as a mechanism of decreasing persistence to antibiotics in preliminary experiments. A top-down analysis was also used to investigate genes associated with chronic infections in nontypeable Haemophilus influenzae (NTHi). A large NTHi genomic dataset containing the whole genome sequences for thousands of clinical isolates, for which clinical metadata are available, is a valuable asset of our lab. Recently, Facebook published a machine learning model for classifying protein variants that had been trained one 250,000,000 proteins. We applied this model to all unique protein sequences in our NTHI genomic collection. These data were used to examine each gene for clusters of variants that correlated with the clinical metadata. A pipeline was developed to assess the strength of correlation with categories including health state, infection type, and patient age. Top hits were identified from significant hits with particular interest in clusters containing a single category of metadata (e.g., only sick isolates).